Design of Bridge Health Monitoring and Safety Assessment System Based on Digital Twins

In this paper, a bridge health monitoring and safety assessment system based on digital twin is designed and implemented. The system adopts a four-layer fusion architecture and is deployed in Nanjing Yangtze River Bridge, where an accurate dynamic digital model is constructed through multiple types of sensing nodes. The improved deep learning algorithm achieves efficient identification and early warning of structural abnormalities. The field application verifies the prediction ability of the system under extreme weather conditions, and successfully captures and analyses the dynamic response characteristics of bridges during typhoons. The research results provide a new type of technical path for the safety management of large bridges, which has significant economic benefits and social value.